# MLE of a function of a parameter

I am working on a problem where we are interested in finding the MLE for a function of two parameters.

I am having problems with going about finding this. Intuitively, the idea makes sense. I am just wondering about the definition of the MLE of a function of two parameters (Google isn't turning up much). The question is as follows:

Question: Suppose that $X_1,\ldots,\,X_n$ are iid $N(\mu, \sigma^2)$ with unknown $\mu,\sigma^2$. Find the MLE for $\frac{\mu}{\sigma}$.

Note that this is not just a homework problem, but part of a take home final. I really am not looking for much of an answer, but more or less the idea for such problems.

Edit

Apparently MLE's are invariant under function. TY

• Assuming you have found the MLE of $\mu$ and $\theta$ you just need to use this theorem. – caburke Dec 10 '12 at 0:36

The question was answered with a link in a comment, so let me just give here the argument from the link, for future completeness.

We assume a statistical model for data $X$ is parameterized by a parameter $\theta$ (which can be scalar or vector, or even more general). Let the likelihood function be $L(\theta)$ and the value of $\theta$ maximizing that be the maximum likelihood estimator $\hat{\theta}$ (mle). We do assume that estimator exists and is unique. Wanted is the mle of $g(\theta)$, a function of $\theta$. First we assume that $g$ is one-to-one. Then we can write $$L(\theta) = L(g^{-1}(g(\theta))$$ and both functions are clearly maximized by $\hat{\theta}$, so $$\hat{\theta} = g^{-1}(\hat{g(\theta)})$$ or $$g(\hat{\theta}) = \hat{g(\theta)}$$ If $g$ is many-to-one, then $\hat{\theta}$ which maximizes $L(\theta)$ still corresponds to $g(\hat{\theta})$, so $g(\hat{\theta})$ still corresponds to the maximum of $L(\theta)$. (Argument paraphrased from the link in the comment above).

I just came up with a solution that I personally like more (for wide range of reasonable estimators):

Assume that $f$ is convex (likelihood function, the solution is unique) and $A$ is monotonous (function of parameter, $\exists A^{-1}$), then $f(A(x))$ is also convex.

What we want to prove is that

$$\arg\max_{x\in X} f(A(x)) = A^{-1}(\arg\max_{z \in A(X)} f(z))$$

Now assume that there exists such a pair $(\tilde x, \tilde z)$ s.t. they are optimal for corresponding problems but $A(\tilde x) \neq \tilde z$.

From optimality: $f(\tilde z) > f(z') \quad \forall z'$ and $f(A(\tilde x)) > f(A(x')) \quad \forall x'$

including $z' = A(\tilde x)$ and $x' = A^{-1}(\tilde z)$, then $$f(\tilde z) > f(A(\tilde x)) > f(A(A^{-1}(\tilde z)) = f(\tilde z)$$ - contradiction!

• more specifically, if you want to get some $\hat g = g(\theta)$ consider a function $A = g^{-1}$ and $f$ is the likelihood $f(\theta)$. – MInner Sep 16 '17 at 0:44